• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.930-935
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• 2015

An unmanned forklift requires precise positioning and pallet detection. Therefore, conventional unmanned forklifts use high-cost sensors to find the exact position of the pallet. In this study, a docking algorithm with two cameras is proposed. The proposed method uses vision data to extract the angle difference between the pallet and the forklift. Then the control law is derived from the extracted angle for successful docking. The extracted angle is compared with the actual angle in the real environment. The control law is tested with the Lyapunov stability test and Routh-Hurwitz stability criterion. Through various experiments, the proposed docking algorithm showed the success rate high enough for real-life applications.

• Journal of Drive and Control
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• v.19 no.4
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• pp.70-76
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• 2022

In this paper, we develops an unmanned test system for the purpose of realizing an actual forklift-based test-bed for the operation durability of the forklift mast. First, two robot actuators were applied to the lever to replace lever manipulation of the operator. For detecting the height of the fork and the tilt angle of the mast, the laser displacement sensor and the inclinometer were installed to the forklift. Next, the embedded control system was used to control the robot actuator with reference to test mode. Experimental evaluation verified that developed test system was effective and practical for the viewpoint of the repeatability of the test mode.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1051-1058
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• 2011

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. Especially, automation of pallet loading and unloading technique is useful for enhancing performance of logistics and reducing cost for automation system. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control, and so on. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. This paper presents a vision sensorbased autonomous loading and unloading for network-based unmanned forklift where system components are connected to a shared CAN network. Functions such as image processing and control algorithm are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. And the experimental results show that proposed architecture can be an appropriate choice for autonomous loading in the unmanned forklift.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.743-744
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• 2010

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.575-576
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• 2009

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.55-56
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• 2011

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.898-904
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• 2010

Unmanned autonomous forklifts have a great potential to enhance the productivity of material handling in various applications because these forklifts can pick up and deliver loads without an operator and any fixed guide. There are, however, many technical difficulties in developing such forklifts including localization, map building, sensor fusion, control and so on. Implementation, which is often neglected, is one of practical issues in developing such an autonomous device. This is because the system requires numerous sensors, actuators, and controllers that need to be connected with each other, and the number of connections grows very rapidly as the number of devices grows. Another requirement on the integration is that the system should allow changes in the system design so that modification and addition of system components can be accommodated without too much effort. This paper presents a network-based distributed approach where system components are connected to a shared CAN network, and control functions are divided into small tasks that are distributed over a number of microcontrollers with a limited computing capacity. This approach is successfully applied to develop an unmanned forklift.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.11a
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• pp.723-724
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• 2010

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.229-231
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• 2018

As autonomous vehicle technology has been gradually developed, robots that have introduced autonomous navigation systems have been actively involved in areas where there is a lot of livelihoods such as industrial sites and accident sites. For this reason, the unmanned transportation system equipped with the autonomous traveling system is widely used in harmful environments where human access is difficult. In addition, the introduction of the autonomous driving system reduces the collision and casualties that occur in a mobility environment like the industrial field, and it helps the efficient work process. In addition, autonomous driving vehicles can be handled more safely and quickly in a wider area by transmitting the surrounding environment of each vehicle to a server connected to each autonomous driving vehicle and passing it through the main server. In this paper, by utilizing V2X communication for autonomous unmanned forklift system, it can increase industrial workload, reduce loss of life and damage to property through wide area forklifts.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1483-1490
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• 2009

This parer is presented multiple path-planning of unmanned autonomous forklift using modified genetic algorithm and fuzzy inference system. There are a task-level feedback method and a method that path is dynamically replaned in realtime while the autonomous vehicles are moving by means of an optimal algorithm for existing multiple path-planning. However, such methods cause malfunctions and inefficiency in the sense of time and energy, and path-planning should be dynamically replanned in realtime. To solve these problems, we propose multiple path-planning using modified genetic algorithm and fuzzy inference system and show the performance with autonomous vehicles. For experiment, we designed and built two autonomous mobile vehicles that equipped with the same driving control part used in actual autonomous forklift, and test the proposed multiple path-planning algorithm. Experimental result that actual autonomous mobile vehicle, we verified that fast optimized path-planning and efficient collision avoidance are possible.